{"id":"W2134392312","doi":"10.1080/01431160903464146","title":"Modelling the vegetation–climate relationship in a boreal mixedwood forest of Alberta using normalized difference and enhanced vegetation indices","year":2011,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":31,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Environmental science; Evapotranspiration; Advanced very-high-resolution radiometer; Vegetation (pathology); Normalized Difference Vegetation Index; Enhanced vegetation index; Moderate-resolution imaging spectroradiometer; Precipitation; Climatology; Boreal; Taiga; Aridity index; Arid; Climate change; Remote sensing; Meteorology; Ecology; Geography; Vegetation Index; Forestry; Geology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003146026,0.000403355,0.000165241,0.0003812799,0.0003748765,0.0005574918,0.000472913,0.0002020653,0.0002668514],"category_scores_gemma":[0.000511794,0.0001552076,0.0001705965,0.000488123,0.0002136735,0.0003154651,0.0002053947,0.0001624231,0.00003797759],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002914371,"about_ca_system_score_gemma":0.002606818,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.7780808,"about_ca_topic_score_gemma":0.8872141,"domain_scores_codex":[0.9999186,0.00001490626,0.000003498859,0.00002007373,0.00002369166,0.00001927984],"domain_scores_gemma":[0.9998735,0.0000504318,0.00002190078,0.000005613567,0.00003169557,0.00001682883],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003953842,0.0002258885,0.3593687,0.00008469317,0.000121682,0.000292616,0.0003009749,0.5846575,0.01467132,0.0007140809,0.0002613409,0.0389058],"study_design_scores_gemma":[0.00002344603,0.00007617835,0.1962525,0.000006575905,0.00004496396,0.00005327472,0.0002735081,0.8002817,0.0021641,0.0002731495,0.0005269859,0.00002358721],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9970376,0.00008489237,0.002292078,0.0000164521,0.00000140053,0.000008046946,0.00009020446,0.00003590441,0.000433459],"genre_scores_gemma":[0.9960373,0.00006711303,0.003374069,0.000005188994,0.000001058989,0.000004698962,0.0001407126,0.000003521455,0.0003664219],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2219192,"threshold_uncertainty_score":0.4464523,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02608409228529886,"score_gpt":0.2461548223358571,"score_spread":0.2200707300505582,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}